![]() In some cases, photogrammetry may seem to be a more economical alternative to LiDAR, especially for small and rapid forest inventory. Photographs taken by digital cameras have become another source of point clouds. Purchasing LiDAR instruments and manual training will incur high costs. However, at present, LiDAR technology is still an expensive operation method in forest resource surveys. This technology fills the gap between tree-scale manual measurements and large airborne LiDAR measurements. Terrestrial laser scanning (TLS) provides a large number of accurate information on forest structure parameters, such as DBH, tree height, and sub-branch height. Airborne laser scanning (ALS) can provide wide coverage, but the accuracy of tree detection and shape is limited. Studies have shown that LiDAR and SfM (structure from motion) are the most effective and accurate methods for individual tree attributes estimation at a medium scale. Researchers have developed a series of algorithms for extracting forest parameters from LiDAR point clouds, opening up new possibilities for forest research. LiDAR, as an active remote sensing technology, has achieved satisfactory results in forest attributes extraction by virtue of its high-precision advantages. Some researchers have made new achievements in the fusion of LiDAR and photogrammetry point clouds. So it is necessary to fully combine the 3D structural features obtained by LiDAR and the texture information obtained from images. The combination of the above two kinds of data can better achieve complementary advantages. Photogrammetry can obtain rich texture and spectral information. LiDAR point clouds lack texture and spectral information, which is of great significance in tree species identification, tree photosynthetic, and non-photosynthetic components separation, etc. ![]() In the past ten years, light detection and ranging (LiDAR) and photogrammetry have received widespread attention as two different point clouds acquisition methods. ![]() Nondestructive obtaining trunk volume with high precision, minimum cost and time is an important content of forestry research. Allometric equation established by destructive sampling is an indirect measurement method to estimate the volume, but the uncertainty of the measurement result is difficult to quantify or even unknown. Cutting down trees is the most direct and accurate, but destructive and costly way to measure trunk volume. The tree model reconstructed by 3D point clouds can be used for quantitative analysis of tree size, tree structure, and other attributes, so as to improve the estimate accuracy of forest stock, above-ground biomass (AGB), and carbon storage.
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